Computer vision involves acquiring, processing, analyzing, and understanding images for use in various applications. Traditionally, an image processor (e.g., an image sensor processor) coupled to an image sensor may acquire image data from the image sensor and perform certain computer vision operations on the acquired image data to detect features in the image data and/or changes among different frames in the image data. The detected features and/or changes may be used in a variety of applications, such as object classification, face recognition, motion detection, object/feature tracking, gesture detection, etc.
Much effort has been made in recent years to enable computing devices to detect motions in the field of view of a computing device, such as a mobile device. Capturing images in the field of view of the computing device using traditional image sensors and processing the captured images to detect motions using conventional processors require significant processing resources, resulting in high power consumption and short battery life per charge cycle in computing devices, which may be very sensitive to power consumption.